arXiv:2606.03241v1 Announce Type: new Abstract: Speech-to-speech translation (S2ST) has advanced rapidly, but offline evaluation lacks a unified protocol: studies report non-overlapping metric subsets, preventing direct comparisons. We introduce COMPASS, a unified and reproducible benchmarking framework integrating 46 metrics across eight dimensions, and deploy it on 1,248 model-language configurations from FLEURS and CVSS, spanning cascaded and end-to-end architectures over ten language pairs. Architectures exhibit complementary strengths: best-vs-worst gaps exceed 30\% on naturalness and spe
The rapid advancement of speech-to-speech translation models necessitates unified benchmarking to enable direct comparisons and accelerate research, addressing current inconsistencies in evaluation protocols.
A standardized benchmarking framework like COMPASS will accelerate the development and deployment of robust S2ST systems, impacting global communication, accessibility, and the practical application of AI in multilingual environments.
The introduction of COMPASS provides a standardized, reproducible method for evaluating S2ST models, allowing researchers and developers to directly compare different architectures and identify strengths and weaknesses more efficiently.
- · AI researchers
- · Speech technology companies
- · Multilingual communications platforms
- · Accessibility technology developers
- · Proprietary, non-standardized evaluation metrics
- · Developers unable to adapt to unified benchmarks
This benchmark will accelerate the pace of innovation and consolidation in the speech-to-speech translation domain.
Improved and more reliable S2ST will enhance global communication, potentially reducing language barriers in business and social interactions.
Enhanced S2ST could lead to more seamless human-AI interaction across languages, underpinning the development of advanced AI agents operating in diverse linguistic contexts.
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